Why Management & Structure Is the Cornerstone of Safer AI Adoption in Healthcare

When implementing new AI models, Healthcare practices should adopt a guiding framework that strategically and securely aligns business goals with implementation efforts.

Key Takeaways

• Successful AI adoption relies on a strong Management & Structure foundation that aligns initiatives with strategic goals, provides clear oversight, and addresses compliance requirements.
• EisnerAmper’s AI Adoption framework offers a comprehensive approach with foundational pillars that span three deployment phases, emphasizing Management & Structure as a crucial first step.
• By embedding structured leadership and comprehensive management within AI initiatives, healthcare organizations can mitigate risks, improve compliance, and enhance trust and transparency.

The AI Adoption Framework: 5 Foundational Pillars Across 3 Phases

At EisnerAmper, we know that successfully navigating AI adoption can be challenging, especially in the healthcare sector. Our AI adoption framework can help you on your journey toward safe integration throughout the full lifecycle. The AI Adoption framework outlines foundational pillars, covering best practices to maintain compliance and mitigate risks, including:

• Management & Structure
• Technology
• Financial
• Compliance & Clinical Risk
• People

These pillars are mapped across three critical phases of AI deployment:

• Readiness & Evaluation (Pre-deployment)
• Testing & Usage (Deployment)
• Monitoring & Validation (Post-deployment)

Each phase demands a different lens, but all rely on a strong Management & Structure as the foundation to succeed. As healthcare organizations accelerate their adoption of artificial intelligence (AI), the stakes have never been higher. From clinical decision support to operational automation, AI promises transformative benefits—but only when deployed with proper guardrails. At the center of this transformation is a foundational pillar that often gets overlooked: Management & Structure.

This pillar isn’t just about oversight—it’s about building a resilient framework that maintains safe, strategic, and sustainable AI initiatives.

Why Management & Structure Must Lead the Way

AI success begins with leadership. The Management & Structure pillar provides the scaffolding for responsible AI adoption, making sure every initiative is aligned, accountable, and risk-aware.

This emphasis is not unique to EisnerAmper’s framework. It is echoed in national guidance from the Joint Commission and the Coalition for Health AI (CHAI), who identify “AI Policies and Governance Structures” as the first foundational elements in their Responsible Use of AI in Healthcare (RUAIH™) framework. Their guidance underscores that:

“Healthcare organizations should establish policies and procedures for implementing and using AI and a governance structure to manage the responsible use of health AI in their organization, including a mechanism to keep the hospital’s governing body updated on uses, outcomes, and potential adverse events.”

 — Joint Commission & CHAI, RUAIH Guidance

This high-level guidance emphasizes the importance of formal structures and policies to oversee the responsible use of AI in healthcare. It aligns with EisnerAmper’s Management & Structure pillar by advocating for designated leadership, cross-functional oversight, and lifecycle risk management of AI tools. The guidance is part of a broader effort to promote a shared understanding of responsible AI deployment and will be operationalized through future playbooks and assurance models.

Building on CHAI & Joint Commission: The Pillar in Practice

This alignment between EisnerAmper’s framework and national guidance reinforces the critical role of structured leadership in AI adoption. Building on this shared foundation, the Management & Structure pillar can be broken down into several key categories that operationalize these principles and bring them to life within healthcare organizations:

Dedicated AI Oversight Structures

Establishing cross-functional and overlapping committees for each critical phase with executive sponsorship ensures that AI decisions are made with strategic and clinical insight, not in silos.

Responsible AI Principles

Embedding ethical guidelines into AI development helps safeguard against bias, misuse, and unintended harm.

Organizational AI Policies & Procedures

Clear, codified policies create consistency across departments and reduce ambiguity in AI use cases.

Tracking and Intake of All Solutions

A centralized intake process enables visibility into all AI tools entering the organization—whether vendor-supplied or internally developed.

Lifecycle Risk Management

From ideation to post-deployment, risk must be continuously assessed and mitigated. This includes legal, operational, and clinical dimensions.

Budget and Funding Oversight

AI initiatives must be financially sound. Structure ensures that funding decisions are tied to strategic priorities and measurable outcomes.

Strategic Alignment and Prioritization

AI should serve the mission—not distract from it. This component helps confirm that projects are prioritized based on impact and feasibility.

Change Management Framework

AI adoption is not a one-time event. A structured change management approach supports continuous learning and adaptation.

Workforce Literacy Initiatives

Educating clinicians and staff about AI builds trust and reduces resistance, paving the way for smoother integration.

Decision-Making Authority

Clear roles and responsibilities streamline operations and empower leaders to act decisively through the committee structures.

Structure Is Not Optional—It’s Foundational

Without strong management and structure, AI adoption can quickly become fragmented, risky, and ineffective. Shadow AI, unclear ROI, and oversight gaps are just a few of the pitfalls that health systems face when structure is lacking.

By anchoring AI initiatives in robust leadership and operational clarity, healthcare organizations can:

• Reduce patient safety risks through ethical and responsible use of technology
• Avoid regulatory penalties through consistent compliance with evolving standards
• Foster greater clinician trust by promoting transparency and accountability
• Demonstrate clear ROI and strategic value by aligning AI projects with organizational goals

Strengthening Leadership Through Structure

As AI becomes more embedded in healthcare operations, IT and digital leaders are increasingly responsible for shaping the policies and structures that guide its use. The Management & Structure pillar equips them with the tools to lead with clarity, ensure accountability, and align AI initiatives with broader organizational goals.

Whether your organization is exploring its first AI use case or expanding across departments, establishing a strong structure is essential. It’s not just about oversight—it’s about enabling informed, strategic leadership that drives meaningful outcomes. With in-depth knowledge and innovative resources, our team can guide you on your journey toward strategic and safe AI adoption. To learn more, contact us below.